> ## Documentation Index
> Fetch the complete documentation index at: https://docs.mindsdb.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Google BigQuery

This documentation describes the integration of MindsDB with [Google BigQuery](https://cloud.google.com/bigquery?hl=en), a fully managed, AI-ready data analytics platform that helps you maximize value from your data.
The integration allows MindsDB to access data stored in the BigQuery warehouse and enhance it with AI capabilities.

## Prerequisites

Before proceeding, ensure the following prerequisites are met:

1. Install MindsDB locally via [Docker](/setup/self-hosted/docker) or [Docker Desktop](/setup/self-hosted/docker-desktop).
2. To connect BigQuery to MindsDB, install the required dependencies following [this instruction](/setup/self-hosted/docker#install-dependencies).

## Connection

Establish a connection to your BigQuery warehouse from MindsDB by executing the following SQL command:

```sql theme={null}
CREATE DATABASE bigquery_datasource
WITH
   engine = "bigquery",
   parameters = {
      "project_id": "bgtest-1111",
      "dataset": "mydataset",
      "service_account_keys": "/tmp/keys.json"
   };
```

Required connection parameters include the following:

* `project_id`: The globally unique identifier for your project in Google Cloud where BigQuery is located.
* `dataset`: The default dataset to connect to.

Optional connection parameters include the following:

* `service_account_keys`: The full path to the service account key file.
* `service_account_json`: The content of a JSON file defined by the `service_account_keys` parameter.

<Note>
  One of `service_account_keys` or `service_account_json` has to be provided to
  establish a connection to BigQuery.
</Note>

## Usage

Retrieve data from a specified table in the default dataset by providing the integration name and table name:

```sql theme={null}
SELECT *
FROM bigquery_datasource.table_name
LIMIT 10;
```

Retrieve data from a specified table in a different dataset by providing the integration name, dataset name and table name:

```sql theme={null}
SELECT *
FROM bigquery_datasource.dataset_name.table_name
LIMIT 10;
```

Run SQL in any supported BigQuery dialect directly on the connected BigQuery database:

```sql theme={null}
SELECT * FROM bigquery_datasource (

   --Native Query Goes Here
   SELECT *
   FROM t1
   WHERE t1.a IN (SELECT t2.a
                  FROM t2 FOR SYSTEM_TIME AS OF t1.timestamp_column);

);
```

<Note>
  The above examples utilize `bigquery_datasource` as the datasource name, which
  is defined in the `CREATE DATABASE` command.
</Note>

## Troubleshooting Guide

<Warning>
  `Database Connection Error`

  * **Symptoms**: Failure to connect MindsDB with the BigQuery warehouse.
  * **Checklist**:
    1. Make sure that the Google Cloud account is active and the Google BigQuery service is enabled.
    2. Confirm that the project ID, dataset and service account credentials are correct. Try a direct BigQuery connection using a client like DBeaver.
    3. Ensure a stable network between MindsDB and Google BigQuery.
</Warning>

<Warning>
  `SQL statement cannot be parsed by mindsdb_sql`

  * **Symptoms**: SQL queries failing or not recognizing table names containing spaces or special characters.
  * **Checklist**:
    1. Ensure table names with spaces or special characters are enclosed in backticks.
       Examples:
       \_ Incorrect: SELECT \_ FROM integration.travel data
       \_ Incorrect: SELECT \_ FROM integration.'travel data'
       \_ Correct: SELECT \_ FROM integration.\`travel data\`
</Warning>